Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Citation - WoS: 9Citation - Scopus: 13Training Cnns With Image Patches for Object Localisation(Institution of Engineering and Technology, 2018) Orhan, Semih; Baştanlar, YalınRecently, convolutional neural networks (CNNs) have shown great performance in different problems of computer vision including object detection and localisation. A novel training approach is proposed for CNNs to localise some animal species whose bodies have distinctive patterns such as leopards and zebras. To learn characteristic patterns, small patches which are taken from different body parts of animals are used to train models. To find object location, in a test image, all locations are visited in a sliding window fashion. Crops are fed into trained CNN and their classification scores are combined into a heat map. Later on, heat maps are converted to bounding box estimates for varying confidence scores. The localisation performance of the patch-based training approach is compared with Faster R-CNN – a state-of-the-art CNN-based object detection and localisation method. Experimental results reveal that the patch-based training outperforms Faster R-CNN, especially for classes with distinctive patterns.Conference Object Citation - Scopus: 6Gender Prediction From Tweets With Convolutional Neural Networks: Notebook for Pan at Clef 2018(CEUR Workshop Proceedings, 2018) Sezerer, Erhan; Polatbilek, Ozan; Sevgili, Özge; Tekir, SelmaThis paper presents a system1 developed for the author profiling task of PAN at CLEF 2018. The system utilizes style-based features to predict the gender information from the given tweets of each user. These features are automatically extracted by Convolutional Neural Networks (CNN). The system mainly depends on the idea that the informativeness of each tweet is not the same in terms of the gender of a user. Thus, the attention mechanism is included to the CNN outputs in order to discriminate the tweets carrying more information. Our architecture was able to obtain competitive results on three languages provided by the PAN 2018 author profiling challenge with an average accuracy of 75.1% on local runs and 70.23% on the submission run.Conference Object Parça Tabanlı Eǧitimin Evrişimli Yapay Sinir Aǧları ile Nesne Konumlandırma Üzerindeki Etkisi(IEEE, 2017) Orhan, Semih; Bastanlar, YalinIn recent years, Convolutional Neural Networks (CNNs) have shown great performance not only in image classification and image recognition tasks but also several tasks of computer vision. A lot of models which have different number of layers and depths, have been proposed. In this work, locations of leopards are tried to be identified by deep neural networks. To accomplish this task, two different methods are applied. First of them is training neural network using with entire images, second of them is training neural networks using with image patches which are cropped from full size of images. Patch training model has shown better performance than full size of image trained model.